title: CT- and computer-based features of small hamartomas.
abstract: OBJECTIVE
  To identify characteristic computed tomographic (CT) and computer-derived features of hamartomas manifesting as small pulmonary nodules.
  
  
  METHODS
  Individuals with a diagnosis of hamartoma were identified among participants in the International Early Lung Cancer Action Program and were included if there thin section CT images that included the entire nodule. The CT findings were reviewed to determine the nodule consistency (solid, part-solid, nonsolid), nodule diameter (average of length and width), shape (round, lobulated, neither) and edge (smooth, not smooth). Computer measures of nodule compactness, sphericity, surface regularity and gradient (change in gray-scale between the nodule and the surrounding parenchyma) were determined. Volume doubling time (VDT) was also determined for those with at least two scans with similar imaging acquisitions.
  
  
  RESULTS
  A total of 21 cases of hamartomas that had histologic or cytologic confirmation were identified. The median age was 60 and 12 (57%) were men. Average diameter was 10.7 mm (5-20.7 mm). All were solid in consistency and were described by the radiologist as having either round or lobulated shape with a smooth edge. None had pathognomonic radiologic findings for hamartoma. Computer measures demonstrated that all were compact and spherical, with a regular surface and a sharp margin between the nodule and surrounding parenchyma. Of nine on whom the VDT could be calculated, eight had VDTs longer than 450 days.
  
  
  CONCLUSIONS
  Both radiologist and computer derived features of small hamartomas suggest a consistent presentation for these lesions which may be helpful in distinguishing them from other types of nodules.
authors.affiliation: Cancer Hospital (Institute), Chinese Academy of Medical Sciences & Peking, Union Medical University, Beijing, PR China.
authors.forename: Yao
authors.initials: Y
authors.lastname: Huang
authors.forename: Dong ming
authors.initials: Dm
authors.lastname: Xu
authors.forename: Artit
authors.initials: A
authors.lastname: Jirapatnakul
authors.forename: Anthony P
authors.initials: AP
authors.lastname: Reeves
authors.forename: Ali
authors.initials: A
authors.lastname: Farooqi
authors.forename: Li juan
authors.initials: Lj
authors.lastname: Zhang
authors.forename: Salvatore
authors.initials: S
authors.lastname: Giunta
authors.forename: Javier
authors.initials: J
authors.lastname: Zulueta
authors.forename: Ralph
authors.initials: R
authors.lastname: Aye
authors.forename: Albert
authors.initials: A
authors.lastname: Miller
authors.forename: David S
authors.initials: DS
authors.lastname: Mendelson
authors.forename: Cheryl
authors.initials: C
authors.lastname: Aylesworth
authors.forename: Barry
authors.initials: B
authors.lastname: Sheppard
authors.forename: Karl
authors.initials: K
authors.lastname: Klingler
authors.forename: David F
authors.initials: DF
authors.lastname: Yankelevitz
authors.forename: Claudia I
authors.initials: CI
authors.lastname: Henschke
mesh_terms: D000368:Aged; D000465:Algorithms; D005260:Female; D006222:Hamartoma / Q000000981:diagnostic imaging*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*